"""simple docstring"""



import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch


__A :     Tuple				       =				logging.get_logger(__name__)






class 	__lowerCAmelCase					:




    '''simple docstring'''






    def __init__(     self				:	int					,				UpperCamelCase__				:	str = None					,				UpperCamelCase__				:	Union[str, Any] = None					,				UpperCamelCase__				:	Any=None					,				UpperCamelCase__				:	List[Any]=None							):
          if not conversation_id:
                A__	:      Tuple      =uuid.uuida()
          if past_user_inputs is None:
                A__	:      List[str]      =[]
          if generated_responses is None:
                A__	:      List[Any]      =[]

          A__	:      uuid.UUID      =conversation_id
          A__	:      List[str]      =past_user_inputs
          A__	:      List[str]      =generated_responses
          A__	:      Optional[str]      =text





    def __eq__(     self				:	Tuple					,				UpperCamelCase__				:	Dict							):
          if not isinstance(__snake_case					,				__snake_case							):
                return False
          if self.uuid == other.uuid:
                return True
          return (
              self.new_user_input == other.new_user_input
              and self.past_user_inputs == other.past_user_inputs
              and self.generated_responses == other.generated_responses
          )





    def 					_UpperCAmelCase		(     self				:	str					,				UpperCamelCase__				:	List[str]					,				UpperCamelCase__				:	Optional[Any] = False							):
          if self.new_user_input:
                if overwrite:
                      logger.warning(
                          F'''User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten '''
                          F'''with: \"{text}\".'''							)
                      A__	:      Union[str, Any]      =text
                else:
                      logger.warning(
                          F'''User input added while unprocessed input was existing: \"{self.new_user_input}\" new input '''
                          F'''ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input'''							)
          else:
                A__	:      Optional[Any]      =text





    def 					_UpperCAmelCase		(     self				:	List[Any]							):
          if self.new_user_input:
                self.past_user_inputs.append(self.new_user_input							)
          A__	:      str      =None





    def 					_UpperCAmelCase		(     self				:	List[str]					,				UpperCamelCase__				:	Tuple							):
          self.generated_responses.append(__snake_case							)





    def 					_UpperCAmelCase		(     self				:	Optional[int]							):
          for user_input, generated_response in zip(self.past_user_inputs					,				self.generated_responses							):
                yield True, user_input
                yield False, generated_response
          if self.new_user_input:
                yield True, self.new_user_input





    def __repr__(     self				:	List[Any]							):
          A__	:      Dict      =F'''Conversation id: {self.uuid} \n'''
          for is_user, text in self.iter_texts():
                A__	:      Any      ="""user""" if is_user else """bot"""
                output += F'''{name} >> {text} \n'''
          return output






@add_end_docstrings(
    _snake_case  ,   R"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """  ,   )
class 	__lowerCAmelCase					(      _snake_case):




    '''simple docstring'''






    def __init__(     self				:	Any					,				*UpperCamelCase__				:	Optional[int]					,				**UpperCamelCase__				:	Dict							):
          super().__init__(*__snake_case					,				**__snake_case							)
          if self.tokenizer.pad_token_id is None:
                A__	:      str      =self.tokenizer.eos_token





    def 					_UpperCAmelCase		(     self				:	List[str]					,				UpperCamelCase__				:	Dict=None					,				UpperCamelCase__				:	Optional[Any]=None					,				UpperCamelCase__				:	Optional[Any]=None					,				**UpperCamelCase__				:	List[str]							):
          A__	:      List[Any]      ={}
          A__	:      str      ={}
          A__	:      Tuple      ={}

          if min_length_for_response is not None:
                A__	:      List[Any]      =min_length_for_response
          if minimum_tokens is not None:
                A__	:      int      =minimum_tokens

          if "max_length" in generate_kwargs:
                A__	:      List[str]      =generate_kwargs["""max_length"""]
                # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
          if clean_up_tokenization_spaces is not None:
                A__	:      str      =clean_up_tokenization_spaces

          if generate_kwargs:
                forward_params.update(__snake_case							)
          return preprocess_params, forward_params, postprocess_params





    def __call__(     self				:	Union[str, Any]					,				UpperCamelCase__				:	List[Any]					,				UpperCamelCase__				:	List[str]=0					,				**UpperCamelCase__				:	List[Any]							):
          A__	:      Dict      =super().__call__(__snake_case					,				num_workers=__snake_case					,				**__snake_case							)
          if isinstance(__snake_case					,				__snake_case							) and len(__snake_case							) == 1:
                return outputs[0]
          return outputs





    def 					_UpperCAmelCase		(     self				:	List[str]					,				UpperCamelCase__				:	Any					,				UpperCamelCase__				:	Union[str, Any]=32							):
          if not isinstance(__snake_case					,				__snake_case							):
                raise ValueError("ConversationalPipeline, expects Conversation as inputs"							)
          if conversation.new_user_input is None:
                raise ValueError(
                    F'''Conversation with UUID {type(conversation.uuid							)} does not contain new user input to process. '''
                    "Add user inputs with the conversation's `add_user_input` method"							)
          if hasattr(self.tokenizer					,				"_build_conversation_input_ids"							):
                A__	:      Optional[Any]      =self.tokenizer._build_conversation_input_ids(__snake_case							)
          else:
                # If the tokenizer cannot handle conversations, we default to only the old version
                A__	:      str      =self._legacy_parse_and_tokenize(__snake_case							)

          if self.framework == "pt":
                A__	:      Optional[Any]      =torch.LongTensor([input_ids]							)
          elif self.framework == "tf":
                A__	:      Optional[Any]      =tf.constant([input_ids]							)
          return {"input_ids": input_ids, "conversation": conversation}





    def 					_UpperCAmelCase		(     self				:	Optional[Any]					,				UpperCamelCase__				:	str					,				UpperCamelCase__				:	Dict=10					,				**UpperCamelCase__				:	Optional[int]							):
          A__	:      Optional[int]      =generate_kwargs.get("max_length"					,				self.model.config.max_length							)

          A__	:      int      =model_inputs["""input_ids"""].shape[1]
          if max_length - minimum_tokens < n:
                logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''							)
                A__	:      Union[str, Any]      =max_length - minimum_tokens
                A__	:      Dict      =model_inputs["""input_ids"""][:, -trim:]
                if "attention_mask" in model_inputs:
                      A__	:      List[str]      =model_inputs["""attention_mask"""][:, -trim:]
          A__	:      Union[str, Any]      =model_inputs.pop("conversation"							)
          A__	:      Dict      =max_length
          A__	:      Any      =self.model.generate(**__snake_case					,				**__snake_case							)
          if self.model.config.is_encoder_decoder:
                A__	:      Dict      =1
          else:
                A__	:      Optional[int]      =n
          return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





    def 					_UpperCAmelCase		(     self				:	Optional[Any]					,				UpperCamelCase__				:	str					,				UpperCamelCase__				:	Optional[Any]=True							):
          A__	:      str      =model_outputs["""output_ids"""]
          A__	:      int      =self.tokenizer.decode(
              output_ids[0]					,				skip_special_tokens=__snake_case					,				clean_up_tokenization_spaces=__snake_case					,				)
          A__	:      Any      =model_outputs["""conversation"""]
          conversation.mark_processed()
          conversation.append_response(__snake_case							)
          return conversation





    def 					_UpperCAmelCase		(     self				:	str					,				UpperCamelCase__				:	Optional[int]							):
          A__	:      int      =self.tokenizer.eos_token_id
          A__	:      Dict      =[]
          for is_user, text in conversation.iter_texts():
                if eos_token_id is not None:
                      input_ids.extend(self.tokenizer.encode(__snake_case					,				add_special_tokens=__snake_case							) + [eos_token_id]							)
                else:
                      input_ids.extend(self.tokenizer.encode(__snake_case					,				add_special_tokens=__snake_case							)							)

          if len(__snake_case							) > self.tokenizer.model_max_length:
                A__	:      List[str]      =input_ids[-self.tokenizer.model_max_length :]
          return input_ids









'''simple docstring'''


from __future__ import annotations







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if len(SCREAMING_SNAKE_CASE__	) <= 1 or n <= 1:
								return

				insert_next(SCREAMING_SNAKE_CASE__				, n - 1	)
				rec_insertion_sort(SCREAMING_SNAKE_CASE__				, n - 1	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if index >= len(SCREAMING_SNAKE_CASE__	) or collection[index - 1] <= collection[index]:
								return

				# Swaps adjacent elements since they are not in ascending order
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= (
				    collection[index],
				    collection[index - 1],
				)

				insert_next(SCREAMING_SNAKE_CASE__				, index + 1	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[str]				  =						input('Enter integers separated by spaces: ')
				UpperCAmelCase_						:  list[int]				  =						[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)




